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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Minimum hardware, reliable processor responds rapidly to changing requirements simply by changes in contents of programmable read-only memory. General purpose controller can transfer data onto and off of data bus, perform logic and arithmetic manipulations, and store pertinent data in small internal random-access memory.
Acceptance testing of explosive components requires a reliable and simple to use testing method that can discern less than optimal performance. For hot-wire detonators, traditional techniques use dent blocks or photographic diagnostic methods. More complicated approaches are avoided because of their inherent problems with setup and maintenance. A recently developed tester is based on using a laser interferometer to measure the velocity of flying plates accelerated by explosively actuated detonators. Unlike ordinary interferometers that monitor displacement of the test article, this device measures velocity directly and is commonly used with non-spectral surfaces. Most often referred to as the VISAR technique (Velocity Interferometer System for Any Reflecting Surface), it has become the most widely-accepted choice for accurate measurement of velocity in the range greater than 1 mm/micro-s. Traditional VISAR devices require extensive setup and adjustment and therefore are unacceptable in a production-testing environment. This paper describes a new VISAR approach which requires virtually no adjustments, yet provides data with accuracy comparable to the more complicated systems. The device, termed the Fixed-Cavity VISAR, is currently being developed to serve as a product verification tool for hot-wire detonators and slappers. An extensive data acquisition and analysis computer code was also created to automate the manipulation of raw data into final results.
Various aspects of residual acceleration data are of interest to low-gravity experimenters. Maximum and mean values and various other statistics can be obtained from data as collected in the time domain. Additional information may be obtained through manipulation of the data. Fourier analysis is discussed as a means of obtaining information about dominant frequency components of a given data window. Transformation of data into different coordinate axes is useful in the analysis of experiments with different orientations and can be achieved by the use of a transformation matrix. Application of such analysis techniques to residual acceleration data provides additional information than what is provided in a time history and increases the effectiveness of post-flight analysis of low-gravity experiments.
The change in the price of energy has encouraged the increase of energy efficiency. This report will discuss a tool to promote energy efficiency in intelligent buildings, energy management control systems (EMCS). In addition to the online control of energy production, supply, and consumption, the function of the EMCS is to support short- and long-term planning of the system operation as well as to collect, store, and regularly evaluate operation data. The strategies behind planning and implementing the EMCS as well as the manipulating the resulting data are discussed in this report.
The present, general-purpose Land Analysis System (LAS) for image processing furnishes a comprehensive set of functions for the manipulation of MSS data; the initial version has more than 240 functions and utilities ranging from pixel manipulation to complex classification. A LAS user's guide describes each such function in terms of purpose, input parameters, illustrative examples, algorithms, error messages, and user codes. The LAS source code is so distributed as to simplify system maintenance; the Transportable Application Executive is employed as the user interface, thereby accommodating both expert and novice users by means of menus and tutored prompting.
The Aerodynamic Data Analysis and Integration System (ADAIS), developed as a highly interactive computer graphics program capable of manipulating large quantities of data such that addressable elements of a data base can be called up for graphic display, compared, curve fit, stored, retrieved, differenced, etc., was described. The general nature of the system is evidenced by the fact that limited usage has already occurred with data bases consisting of thermodynamic, basic loads, and flight dynamics data. Productivity using ADAIS of five times that for conventional manual methods of wind tunnel data analysis is routinely achieved. In wind tunnel data analysis, data from one or more runs of a particular test may be called up and displayed along with data from one or more runs of a different test. Curves may be faired through the data points by any of four methods, including cubic spline and least squares polynomial fit up to seventh order.
The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.
A survey of the meteoroid and space debris impacts on LDEF experiment M0003 was performed. The purpose was to document impact phenomenology and to obtain impact crater data for comparison to current debris and micrometeoroid models. The survey consists of photomicrographs of significant impacts in a variety of material types, accurate measurements of impact crater coordinates and dimensions for selected experiment surfaces and data basing of the crater data for reduction, manipulation, and comparison models. Large area surfaces which were studied include the experiment power and data system sunshields, environment exposure control canister sunshields, and the M0003 signal conditioning unit covers. Crater diameters down to 25 microns were measured and catalogued. Both leading and trailing edge surfaces were studied and compared. The results shows the different response of materials to hypervelocity impacts. Comparison of the data to curves derived from the Kessler debris model and the Cour-Palais micrometeoroid model indicates a marked tendency for overprediction of small impacts and underprediction of large impacts with there being better agreement for the medium sized impacts. Representative impact data is presented.
This paper describes the implementation and potential applications of a workstation-based orbit determination system developed by Storm Integration, Inc. called the Precision Orbit Determination System (PODS). PODS is offered as a layered product to the commercially-available Satellite Tool Kit (STK) produced by Analytical Graphics, Inc. PODS also incorporates the Workstation/Precision Orbit Determination (WS/POD) product offered by Van Martin System, Inc. The STK graphical user interface is used to access and invoke the PODS capabilities and to display the results. WS/POD is used to compute a best-fit solution to user-supplied tracking data. PODS provides the capability to simultaneously estimate the orbits of up to 99 satellites based on a wide variety of observation types including angles, range, range rate, and Global Positioning System (GPS) data. PODS can also estimate ground facility locations, Earth geopotential model coefficients, solar pressure and atmospheric drag parameters, and observation data biases. All determined data is automatically incorporated into the STK data base, which allows storage, manipulation and export of the data to other applications. PODS is offered in three levels: Standard, Basic GPS and Extended GPS. Standard allows processing of non-GPS observation types for any number of vehicles and facilities. Basic GPS adds processing of GPS pseudo-ranging data to the Standard capabilities. Extended GPS adds the ability to process GPS carrier phase data.
A survey of the meteoroid and space debris impacts on LDEF experiment M0003 was performed. The purpose of this survey was to document significant impact phenomenology and to obtain impact crater data for comparison to current space debris and micrometeoroid models. The survey consists of the following: photomicrographs of significant impacts in a variety of material types; accurate measurements of impact crater coordinates and dimensions for selected experiment surfaces; and databasing of the crater data for reduction, manipulation, and comparison to models. Large area surfaces that were studied include the experiment power and data system (EPDS) sunshields, environment exposure control canister (EECC) sunshields, and the M0003 signal conditioning unit (SCU) covers. Crater diameters down to 25 microns were measured and cataloged. Both leading (D8) and trailing (D4) edge surfaces were studied and compared. The EPDS sunshields are aluminum panels painted with Chemglaze A-276 white thermal control paint, the EECC sunshields are chromic acid-anodized aluminum, and the SCU covers are aluminum painted with S13GLO white thermal control paint. Typical materials that have documented impacts are metals, glasses and ceramics, composites, polymers, electronic materials, and paints. The results of this survey demonstrate the different response of materials to hypervelocity impacts. Comparison of the survey data to curves derived from the Kessler debris model and the Cour-Palais micrometeoroid model indicates that these models overpredict small impacts (less than 100 micron) and may underpredict large impacts (greater than 1000 micron) while having fair to good agreement for the intermediate impacts. Comparison of the impact distributions among the various surfaces indicates significant variations, which may be a function of material response effects, or in some cases surface roughness. Representative photographs and summary graphs of the impact data are presented.
We seek to combine high-resolution remotely sensed data with models and ground truth measurements, in the context of a Geographical Information System, integrated with specialized image processing software. We will use this integrated system to analyze the data from two Case Studies, one at a bore Al forest site, the other a tropical forest site. We will assess the information content of the different components of the data, determine the optimum data combinations to study biogeophysical changes in the forest, assess the best way to visualize the results, and validate the models for the forest response to different radar wavelengths/polarizations. During the 1990's, unprecedented amounts of high-resolution images from space of the Earth's surface will become available to the applications scientist from the LANDSAT/TM series, European and Japanese ERS-1 satellites, RADARSAT and SIR-C missions. When the Earth Observation Systems (EOS) program is operational, the amount of data available for a particular site can only increase. The interdisciplinary scientist, seeking to use data from various sensors to study his site of interest, may be faced with massive difficulties in manipulating such large data sets, assessing their information content, determining the optimum combinations of data to study a particular parameter, visualizing his results and validating his model of the surface. The techniques to deal with these problems are also needed to support the analysis of data from NASA's current program of Multi-sensor Airborne Campaigns, which will also generate large volumes of data. In the Case Studies outlined in this proposal, we will have somewhat unique data sets. For the Bonanza Creek Experimental Forest (Case I) calibrated DC-8 SAR data and extensive ground truth measurement are already at our disposal. The data set shows documented evidence to temporal change. The Belize Forest Experiment (Case II) will produce calibrated DC-8 SAR and AVIRIS data, together with extensive measurements on the tropical rain forest itself. The extreme range of these sites, one an Arctic forest, the other a tropical rain forest, has been deliberately chosen to find common problems which can lead to generalized observations and unique problems with data which raise issues for the EOS System.
We seek to combine high-resolution remotely sensed data with models and ground truth measurements, in the context of a Geographical Information System (GIS), integrated with specialized image processing software. We will use this integrated system to analyze the data from two Case Studies, one at a boreal forest site, the other a tropical forest site. We will assess the information content of the different components of the data, determine the optimum data combinations to study biogeophysical changes in the forest, assess the best way to visualize the results, and validate the models for the forest response to different radar wavelengths/polarizations. During the 1990's, unprecedented amounts of high-resolution images from space of the Earth's surface will become available to the applications scientist from the LANDSAT/TM series, European and Japanese ERS-1 satellites, RADARSAT and SIR-C missions. When the Earth Observation Systems (EOS) program is operational, the amount of data available for a particular site can only increase. The interdisciplinary scientist, seeking to use data from various sensors to study his site of interest, may be faced with massive difficulties in manipulating such large data sets, assessing their information content, determining the optimum combinations of data to study a particular parameter, visualizing his results and validating his model of the surface. The techniques to deal with these problems are also needed to support the analysis of data from NASA's current program of Multi-sensor Airborne Campaigns, which will also generate large volumes of data. In the Case Studies outlined in this proposal, we will have somewhat unique data sets. For the Bonanza Creek Experimental Forest (Case 1) calibrated DC-8 SAR (Synthetic Aperture Radar) data and extensive ground truth measurement are already at our disposal. The data set shows documented evidence to temporal change. The Belize Forest Experiment (Case 2) will produce calibrated DC-8 SAR and AVIRIS data, together with extensive measurements on the tropical rain forest itself. The extreme range of these sites, one an Arctic forest, the other a tropical rain forest, has been deliberately chosen to find common problems which can lead to generalized observations and unique problems with data which raise issues for the EOS System.
Several problems posed by the rapidly growing volume of geophysical data are described, and a selected set of existing solutions to these problems is outlined. A recently developed desktop software tool called the Grid Analysis and Display System (GrADS) is presented. The GrADS' user interface is a natural extension of the standard procedures scientists apply to their geophysical data analysis problems. The basic GrADS operations have defaults that naturally map to data analysis actions, and there is a programmable interface for customizing data access and manipulation. The fundamental concept of the GrADS' dimension environment, which defines both the space in which the geophysical data reside and the 'slice' of data which is being analyzed at a given time, is expressed The GrADS' data storage and access model is described. An argument is made in favor of describable data formats rather than standard data formats. The manner in which GrADS users may perform operations on their data and display the results is also described. It is argued that two-dimensional graphics provides a powerful quantitative data analysis tool whose value is underestimated in the current development environment which emphasizes three dimensional structure modeling.
The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more complexities to data engineering applications, which are now integrated into data processing pipelines to process terabytes of data. Typically, a significant amount of time is spent on data preprocessing in these pipelines, and hence improving its efficiency directly impacts the overall pipeline performance. The community has recently embraced the concept of Dataframes as the de-facto data structure for data representation and manipulation. However, the most widely used serial Dataframes today (R, pandas) experience performance limitations while working on even moderately large data sets. We believe that there is plenty of room for improvement by taking a look at this problem from a high-performance computing point of view. In a prior publication, we presented a set of parallel processing patterns for distributed dataframe operators and the reference runtime implementation, Cylon. In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns. Furthermore, we evaluate the performance of Cylon on the ORNL Summit supercomputer.
Recently there has been interest in the incorporation of core Flight Systems (cFS) with Spacecraft Onboard Interface Services (SOIS) Electronic Data Sheets (EDS) in the spaceflight software community. The Regenerative Fuel Cell project at the Glenn Research Center is using cFS architecture with EDS support for its monitoring and control software. The presentation will outline the benefits to using cFS with EDS support: First, EDS establishes a single source of truth for the definitions of data structures used throughout an entire mission that may otherwise be programmed in different languages and designed with different processor architectures. Not only does this help with inter-application communication via the software bus, but it also greatly simplifies communication between systems. An EDS Application Programming Interface (API) library allows the conversion of EDS data structures to and from native data structures. Second, bindings for other programming languages (e.g. Lua, Python, JSON) have been written to allow the creation and manipulation of EDS data objects within those languages. The RFC project uses Lua scripts to automatically generate binary configuration files at build time to be loaded into our cFS programs. We also use Python bindings in a graphical user interface (GUI) to allow an operator to send commands and view telemetry messages sent from cFS instances. Finally, using Lua scripts we can set up specific simulation scenarios to perform automatic functional testing. During the development of the RFC software, the software team put together a generic python GUI called “cFS-EDS-GroundStation” that provides a basic interface to an instance of cFS with EDS support. The GUI includes a basic telecommand and telemetry system that reads directly from the generated EDS databases. In the telecommand system, dropdown menus are populated with all user commands that are defined in EDS. In the telemetry system, telemetry messages are automatically decoded, written to the screen, and saved to a binary file. Additional Python scripts have been written to convert the binary data files into a comma separated value (CSV) format for further processing. We will demonstrate the basic use of the cFS-EDS-GroundStation software including adding additional commands and telemetry payload values in EDS and see them appear automatically in the cFS-EDS-Groundstation software. About the RFC project: The Regenerative Fuel Cell project is tasked with developing and demonstrating a power system consisting of a fuel cell and electrolyzer to provide power during a lunar day/night cycle. During the night, the fuel cell takes Hydrogen and Oxygen gasses and converts them into electricity, water, and heat. During the day, the electrolyzer takes input power (e.g. from a photovoltaic array) and converts water back into Hydrogen and Oxygen gasses.
Increasingly sophisticated National Aeronautics and Space Administration (NASA) Earth science missions have driven their associated data and data management systems from providing simple point-to-point archiving and retrieval to performing user-responsive distributed multisensor information extraction. To fully maximize the use of remote-sensor-generated Earth science data, NASA recognized the need for data systems that provide data access and manipulation capabilities responsive to research brought forth by advancing scientific analysis and the need to maximize the use and usability of the data. The decision by NASA to purposely evolve the Earth Observing System Data and Information System (EOSDIS) at the Goddard Space Flight Center (GSFC) Earth Sciences (GES) Data and Information Services Center (DISC) and other information management facilities was timely and appropriate. The GES DISC evolution was focused on replacing the EOSDIS Core System (ECS) by reusing the In-house developed disk-based Simple, Scalable, Script-based Science Product Archive (S4PA) data management system and migrating data to the disk archives. Transition was completed in December 2007
Johnson Space Center's (JSC's) indentured parts list (IPL) maintenance and parts assembly capture tool (IMPACT) is an easy-to-use graphical interface for viewing and maintaining the complex assembly hierarchies of large databases. IMPACT, already in use at JSC to support the International Space Station (ISS), queries, updates, modifies, and views data in IPL and associated resource data, functions that it can also perform, with modification, for any large commercial database. By enabling its users to efficiently view and manipulate IPL hierarchical data, IMPACT performs a function unlike that of any other tool. Through IMPACT, users will achieve results quickly, efficiently, and cost effectively.
Striations in Czochralski (CZ) grown crystals have been observed in materials such as GaAs, silicon, photorefractive crystals used for data storage, potassium titanyl phosphate crystals and LiNbO3. Several techniques have been used for investigating these defects including electron microscopy, laser scanning tomography, selective photoetching, X-ray diffuse scattering, interference orthoscopy, laser interferometry and micro-Fourier transform infrared spectroscopy mapping. A 2mm thick sample of the material to be investigated is illuminated with light that is absorbed and non-absorbed by the ion concentration to be observed. The back surface of the sample is focused onto a solid-state image detector and images of the input beam and absorbed (and diffracted) beams are captured at two wavelengths. The variation of the coefficient of absorption asa function of distance on the sample can be derived from these measurements. A Big Sky Software Beamcode system is used to capture and display images. Software has been written to convert the Beamcode data files to a format that can be imported into a spreadsheet program such as Quatro Pro. The spreadsheet is then used to manipulate and display data. A model of the intensity map of the striae collected by the imaging system has been proposed and a data analysis procedure derived. From this, the variability of the attenuation coefficient alpha can be generated. Preliminary results show that alpha may vary by a factor of four or five over distances of 100 mu m. Potential errors and problems have been discovered and additional experiments and improvements to the experimental setup are in progress and we must now show that the measurement techniques and data analysis procedures provide 'real' information. Striae are clearly visible at all wavelengths including white light. Their basic spatial frequency does not change radically, at least when changing from blue to green to white light. Further experimental and theoretical work can be done to improve the data collection techniques and to verify the data analysis procedures.